System and method for electrical and magnetic monitoring of a material

ABSTRACT

A system and method for monitoring a characteristic of a material by measuring electrical or magnetic properties of the material. The system includes a material monitoring device having at least one electrode and at least one magnetic coil, and is in communication with a machine learning model trained to recognize characteristics of the material based on electrical and magnetic properties of the material. The material can be stimulated with an electrical stimulus or stimulating magnetic field, and an electrical response signal or magnetic response signal can be measured. Applications to monitoring water quality, beverages, foodstuffs, and other characteristics of materials is discussed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 16/990,716, filed on Aug. 11, 2020, which claimspriority to U.S. patent application Ser. No. 15/815,014, filed on Nov.16, 2017, which claims priority to U.S. 62/422,774, filed Nov. 16, 2016,all of which is incorporated herein by reference.

FIELD

The present disclosure relates generally to material monitoring.

BACKGROUND

There are many materials used today that have characteristics thatchange over time, have the potential to expire, or may be contaminated.Consumers generally do not have a reliable means of monitoring thecurrent status and characteristics of these products before or afterpurchasing or delivery. One such class of products is water that can bedelivered by plumbing or water bottles. Potential problems with waterinclude contamination, whether in a municipal water distribution systemor in a water packaging facility. Another class of such products isbeverages, especially wines, which are known to change characteristicsover time, including characteristics relevant to taste of the wine.Another class of such products is foodstuffs. A common problem withbeverage and foodstuff products is that these products may spoil,decompose, or proceed past their ideal period for consumption, maturitypoint, or peak flavor point.

For water, a consumer typically relies on municipal water treatmentsystems or quality control in the water bottle packaging facility. Forbeverage products and foodstuffs, some manufacturers provide anestimated “best before” date or a date on which the product wasproduced, which serves as a crude benchmark for estimating when aproduct has spoiled or passed its ideal consumption point. The typicalconsumer relying on these dates, however, must trust that the productcontained within the packaging is still in good condition uponconsumption and that it will match the characteristics advertised by themanufacturer.

Another class of materials that experiences relevant changes incharacteristics over time are chemical products. The changes may beinduced by environmental factors or they may occur spontaneously. Theymay be due to physical process changes such as evaporation or on-goingchemical reaction processes such as ion exchange or other reactions. Achemical substance may only be useful to the purchaser when it possessescharacteristics within a particular range.

Current solutions to monitoring water, beverages, foodstuffs, andsimilar materials typically involve invasive testing of the product ormeasurements performed on gas/vapor given off by the product. Manysolutions require that the container be opened, thus altering theproduct's state or in many cases accelerating the spoiling process.Further, solutions that reference the gas/vapor given off by the productare indirect and may have reduced accuracy or may be incapable ofmeasuring the desired characteristics.

SUMMARY

According to an aspect of the disclosure, a system for monitoring acharacteristic of a material includes a sensor device, the sensor deviceincluding at least one electrode, the at least one electrode configuredto contact the material and to apply an electrical stimulus to thematerial and measure an electrical response signal of the material, andat least one magnetic coil, the at least one magnetic coil configured toapply a stimulating magnetic field to the material and measure amagnetic response signal, a computing device configured to apply machinelearning for determining a not directly measurable characteristic of thematerial based on at least the electrical response signal and themagnetic response signal, wherein at least one of the electricalresponse signal and the magnetic response signal is influenced by atleast one of the electrical stimulus and the stimulating magnetic fieldaltered by the material, and wherein the machine learning applied via amachine learning model trained with library data to recognize the notdirectly measurable characteristic of the material, the library datarelating at least one of a previously measured electrical responsesignal and a previously measured magnetic response signal to a known notdirectly measurable characteristic of the material, a circuit connectingthe sensor device and computing device, and a body housing the sensordevice.

In some embodiments, the electrical stimulus is generated bytransmitting an initiating electrical signal to the at least oneelectrode, and the stimulating magnetic field is generated bytransmitting the initiating electrical signal to the at least onemagnetic coil.

In some embodiments, the initiating electrical signal includes a varyingsignal profile.

In some embodiments, at least one of the electrical response signal andthe magnetic response signal is transformed into a transformed signalprofile, and the machine learning is applied to the transformed signalprofile.

In some embodiments, the stimulating magnetic field includes asinusoidal oscillating signal.

In some embodiments, the at least one electrode includes an inputelectrode and an output electrode, and the output electrode isconfigured to apply the electrical stimulus to the material, and theinput electrode is configured to measure the electrical response signal.

In some embodiments, the at least one magnetic coil includes an inputmagnetic coil and an output magnetic coil, and the output magnetic coilis configured to apply the stimulating magnetic field to the material,and the input magnetic coil is configured to measure the magneticresponse signal.

In some embodiments, the system further includes a material conduit, thematerial conduit defining an interior for transporting the material, thebody housing the sensor device is attachable to the material conduit,and the at least one electrode of the sensor device extending into theinterior of the material conduit.

According to another aspect of the disclosure, a system for monitoring acharacteristic of a material includes a sensor device, the sensor deviceincluding at least one electrode, the at least one electrode configuredto contact the material and to measure an electrical response signal,and at least one magnetic coil, the at least one magnetic coilconfigured to apply a stimulating magnetic field to the material and tomeasure a magnetic response signal, a computing device configured toapply machine learning for determining a not directly measurablecharacteristic of the material based on at least the electrical responsesignal and the magnetic response signal, wherein at least one of theelectrical response signal and the magnetic response signal isinfluenced by the stimulating magnetic field altered by the material,and wherein the machine learning applied via a machine learning modeltrained with library data to recognize the not directly measurablecharacteristic of the material, the library data relating at least oneof a previously measured electrical response signal and a previouslymeasured magnetic response signal to a known not directly measurablecharacteristic of the material, a circuit connecting the sensor deviceand computing device, and a body housing the sensor device.

In some embodiments, the stimulating magnetic field is generated bytransmitting an initiating electrical signal to the at least onemagnetic coil, the initiating electrical signal including a varyingsignal profile.

In some embodiments, the magnetic response signal is transformed into atransformed signal profile, and the machine learning is applied to thetransformed signal profile.

In some embodiments, the stimulating magnetic field includes ansinusoidal oscillating signal.

In some embodiments, the at least one magnetic coil includes an inputmagnetic coil and an output magnetic coil, and wherein the outputmagnetic coil is configured to apply the stimulating magnetic field tothe material, and the input magnetic coil is configured to measure themagnetic response signal.

In some embodiments, the system further includes a material conduit, thematerial conduit defining an interior for transporting the material,wherein the body housing the sensor device is attachable to the materialconduit, the at least one electrode of the sensor device extending intothe interior of the material conduit.

According to another aspect of the disclosure, a system for monitoring acharacteristic of a material includes a sensor device, the sensor deviceincluding at least one electrode, the at least one electrode configuredto contact the material and to apply an electrical stimulus to thematerial, and at least one magnetic coil, the at least one magnetic coilconfigured to apply a stimulating magnetic field to the material and tomeasure a magnetic response signal, a computing device configured toapply machine learning for determining a not directly measurablecharacteristic of the material based on at least the magnetic responsesignal, wherein the magnetic response signal is influenced by at leastone of the electrical stimulus and the stimulating magnetic fieldaltered by the material, and wherein the machine learning applied via amachine learning model trained with library data to recognize the notdirectly measurable characteristic of the material, the library datarelating at least one of a previously measured electrical responsesignal and a previously measured magnetic response signal to a known notdirectly measurable characteristic of the material, a circuit connectingthe sensor device and computing device; and a body housing the sensordevice.

In some embodiments, the electrical stimulus is generated bytransmitting an initiating electrical signal to the at least oneelectrode, and the stimulating magnetic field is generated bytransmitting the initiating electrical signal to the at least onemagnetic coil, and wherein the initiating electrical signal comprises avarying signal profile.

In some embodiments, the magnetic response signal is transformed into atransformed signal profile, and the machine learning is applied to thetransformed signal profile.

In some embodiments, the at least one magnetic coil includes an inputmagnetic coil and an output magnetic coil, and wherein the outputmagnetic coil is configured to apply the stimulating magnetic field tothe material, and the input magnetic coil is configured to measure themagnetic response signal.

In some embodiments, the system further includes a material conduit, thematerial conduit defining an interior for transporting the material, thebody housing the sensor device is attachable to the material conduit,the at least one electrode of the sensor device extending into theinterior of the material conduit.

According to another aspect of the disclosure, a system for monitoring acharacteristic of a material includes a sensor device, the sensor deviceincluding at least one magnetic coil, the at least one magnetic coilconfigured to apply a stimulating magnetic field to the material and tomeasure a magnetic response signal, a computing device configured toapply machine learning for determining a not directly measurablecharacteristic of the material based on at least the magnetic responsesignal, wherein at least the magnetic response signal is influenced bythe stimulating magnetic field altered by the material, and wherein themachine learning applied via a machine learning model trained withlibrary data to recognize the not directly measurable characteristic ofthe material, the library data relating at least one of a previouslymeasured magnetic response signal to a known not directly measurablecharacteristic of the material, a circuit connecting the sensor deviceand computing device, and a body housing the sensor device.

In some embodiments, the sensor device further includes at least oneelectrode, the at least one electrode configured to contact the materialand to measure an electrical response signal, the computing device isconfigured to apply machine learning for determining a not directlymeasurable characteristic of the material based on at least theelectrical response signal and the magnetic response signal, at leastone of the electrical response signal and the magnetic response signalis influenced by the stimulating magnetic field altered by the material,and the library data relates at least one of a previously measuredelectrical response signal and a previously measured magnetic responsesignal to a known not directly measurable characteristic of thematerial.

Other features and advantages are described below.

BRIEF DESCRIPTION OF THE DRAWINGS

Preferred embodiments of the present disclosure will now be described,by way of example only, with reference to the attached Figures, wherein:

FIG. 1 depicts a schematic diagram of a system for monitoringcharacteristics of a material, according to a non-limiting embodiment;

FIG. 2 depicts a perspective view of a device for monitoringcharacteristics of a material, according to a non-limiting embodiment;

FIG. 3 depicts a functional block diagram of the device of FIG. 2;

FIG. 4 depicts a flowchart of a method for determining a characteristicof a material, according to a non-limiting embodiment;

FIG. 5 depicts a schematic diagram of the generation and measurement ofelectrical and magnetic signals for use in a machine learning model;

FIG. 6 depicts a flowchart of a method for initializing a device formonitoring characteristics of a material, according to a non-limitingembodiment;

FIG. 7 depicts a functional block diagram of a device for monitoringcharacteristics of a material, according to another non-limitingembodiment;

FIG. 8 depicts a schematic diagram of a system for monitoringcharacteristics of a material, according to another non-limitingembodiment; and

FIG. 9 depicts a perspective view of a device for monitoringcharacteristics of a material, according to another non-limitingembodiment.

DETAILED DESCRIPTION

The disclosure relates to a system and method for monitoring acharacteristic of a material by measuring electrical or magneticproperties of the material. The system includes a material monitoringdevice having at least one electrode and at least one magnetic coil, andis in communication with a machine learning model trained to recognizecharacteristics of the material based on electrical and magneticproperties of the material. The material can be stimulated with anelectrical stimulus or stimulating magnetic field, and an electricalresponse signal or magnetic response signal can be measured. Thisdisclosure discusses applications to the monitoring of water quality,beverages, foodstuffs, and other materials.

The system may include a computing device hosting a database and amachine learning model, or may include a cloud computing environmenthaving a distributed database and a machine learning model. The materialmonitoring device can thereby be made with minimal storage andprocessing capabilities, with storage and processing duties beinghandled by an external cloud computing device or cloud computingenvironment, allowing for efficient energy operation of the materialmonitoring device.

The material monitoring device can be made sufficiently compact to beable to directly take measurements inside small conduits and vesselscontaining materials. For example, the material monitoring device maytake measurements along a water faucet or water material. As anotherexample, the material monitoring device may be at least partly housedwithin a cork of a wine bottle and may take measurements from the winein the wine bottle.

The material monitoring device includes at least one electrode and atleast one magnetic coil for measuring electrical and magnetic signalsfrom the material. In some implementations, an electrode may provide anelectrical stimulus to the material, or a stimulating magnetic field tothe material, to stimulate the electrical or magnetic signal measuredfrom the material. In some implementations, a plurality of electrodes,or a plurality of magnetic coils, may be used, with some electrodes ormagnetic coils being dedicated to providing a stimulus, with othersbeing dedicated to measurement. The stimuli and response signals may beincorporated into the machine learning model for training and fordetermining a characteristic of the material.

Additionally, the material monitoring device can be made with electrodesthat can be in direct contact with the material being monitored,improving the electrical connection with the material and thereby theaccuracy of any electrical measurement taken, without disturbing thematerial by requiring the vessel to be opened for inspection. Similarly,the material monitoring device can be made with magnetic coils inproximity to the material being monitored.

A library relating previously measured electrical measurements andmagnetic measurements of materials to characteristics of those materialscan be developed to train a machine learning model to recognizecharacteristics of those materials based on electrical or magneticsignal profile measurements of those materials. A machine learning modelcan thereby be trained to recognized characteristics of a material whichare not directly measurable by conventional or practical means. Thus, amachine learning model can be trained to recognize a not directlymeasurable characteristic of a material. For example, it may not befeasible to conduct sample gathering and laboratory analysis of a home'swater supply on a continual basis to determine the presence of acontaminant in the water, and thus such a procedure may be sufficientlylengthy and cumbersome such that the presence of the contaminant isconsidered not directly measurable. However, by application of thesystem described herein, a machine learning model may be trained torecognize that measurement of, for example, a particular magnetic signalprofile from water flowing through a water faucet, following aparticular electrical stimulus, indicates the presence of a contaminant,such as a microbe contaminant, in a home's water supply. A water qualitymonitoring device installed on the home water faucet may thereby beconfigured to immediately indicate such contamination. As anotherexample, it may become recognized that the measured electrical impedanceof wine may be related to the development of a particular flavor of thewine throughout its aging process. In other examples, a beneficialcharacteristic of water or other material may be monitored, such as thequantity of a nutrient, a preferable level of mineral, the presence of abeneficial microbe, etc.

Other features and advantages of the system are described more fullybelow, where non-limiting embodiments of the system are described withreference to the following Figures. For convenience, reference numeralsmay be repeated (with or without an offset) to indicate analogouscomponents or features.

FIG. 1 shows a system 100 for monitoring a material 105, according to anon-limiting embodiment. The system 100 includes one or more materialconduits 110 delivering a material 105. The system 100 includes amaterial monitoring device 200 attached to a material conduit 110monitoring the material 105 passing through material conduit 110.

In the present embodiment, the material 105 being monitored comprisestap water passing through a water conduit such as a water pipe or awater faucet. The material monitoring device 200 is located at theopening 115 of the material conduit 110.

The material monitoring device 200 is in communication with a wirelessdevice 130. The wireless device 130 is in communication over network 150with one or more computing devices 160 storing a database 170. Thenetwork 150 can include a wireless cellular data network, a Wi-Finetwork, a local-area network, a wide-area network (WAN), a Bluetoothpairing or connection, the internet, a virtual private network (VPN), acombination of such, and similar. The database 170 stores measurementdata 172 and library data 174, discussed in greater detail below.

Briefly, the material monitoring device 200 measures electrical andmagnetic properties of the material 105 and transmits the results asmeasurement data 172, which may include other ancillary data, includingdata related to any electrical or magnetic stimulus, to the wirelessdevice 130.

The wireless device 130 is in communication with the computing device160 which stores the database 170. Measurement data 172 is periodicallytransmitted by the material monitoring device 200 to the wireless device130, which in turn transmits the measurement data 172 to the computingdevice 160. The library data 174 stores existing data relating one ormore electrical properties or magnetic properties of a material 105 tocharacteristics of the material 105.

The computing device 160 is configured to compute, correlate, orotherwise determine a characteristic of the material 105 by comparingthe measured electrical properties or magnetic properties of thematerial 105 in measured data 172 to library data 174. The computingdevice 160 can communicate an indication of this characteristic or thecharacteristic itself to interested parties (not shown), such as aconsumer, owner, retailer, or manufacturer across the network 150,whether through the wireless device 130 or otherwise. In someembodiments, an indication that a characteristic has reached a thresholdcan be transmitted as an alert to the wireless device 130.

Some characteristics, although not measurable directly, can berecognized by a machine learning model incorporating measurement data172 and library data 174, which relates electrical and magneticproperties of water to known, not directly measurable, characteristicsof water. For example, a machine learning model may be trained torecognize that measurement of a particular magnetic signal profile fromwater flowing through a water faucet, following a particular electricalstimulus, indicates the presence of a contaminant such as a microbecontaminant, a chemical contaminant, a metal contaminant such as lead, amineral contaminant, or other contaminant in a home's water supply.Thus, where a characteristic is not directly measurable, such as, in thecase of a contaminant, where detection of the contaminant may involve asufficiently lengthy and cumbersome process such that the presence ofthe contaminant is considered not directly measurable, a machinelearning model may be trained to recognize the not directly measurablecharacteristic with library data 174 relating previously measuredelectrical or magnetic properties of the material to where the presenceof the contaminant is known. Thus, the library data may relatepreviously measured electrical or magnetic properties to known notdirectly measurable characteristics of the material. For example, thelibrary data 174 may include magnetic signal profiles which indicate thepresence of a particular microbe contaminant, or library data 174 mayinclude electrical signal profiles which indicate a quantity ofchemical. In some examples, an electrical or magnetic signal mayindicate the presence of a beneficial compound, such as a nutrient, apreferable level of mineral, a beneficial microbe, etc. The machinelearning model and signal analysis are discussed in greater detail belowwith reference to FIG. 5.

In the present embodiment, the wireless device 130 includes a smartphone running an operating system such as, for example, Android®, iOS®,Windows® mobile, BB 10, or similar. The wireless device 130 receivesalerts and indications from the computing device 160 regardingcharacteristics of the material 105, thereby serving as an end-userdevice for monitoring a material.

In other embodiments, the wireless device 130 includes a tabletcomputer, a personal digital assistant (PDA), computer, or other machinewith communications ability within range of the material monitoringdevice 200. In these embodiments, the wireless device 130 similarlyserves as an end-user device for monitoring a material.

In still other embodiments, the wireless device 130 includes a wirelessaccess point, wireless router, or similar network device. In theseembodiments, a computing device 160 serves as an end-user device formonitoring a material.

In still other embodiments, a first computing device 160 is incommunication with a second computing device 160, the second computingdevice 160 serving as an end-user device for monitoring a material.

In the present embodiment, an computing device 160 includes a computingdevice running a server application with storage, communication, andprocessing means.

A person skilled in the art upon reading this specification willappreciate that the wireless device 130 and the computing device 160 caneach be more generally referred to as external computing devices, andthat in certain embodiments the responsibility of each externalcomputing device may be interchangeable. In the present embodiment,measurement data 172 is transmitted from the material monitoring device200, temporarily stored on the wireless device 130, and transmitted to acomputing device 160 for permanent storage on database 170, forcomputation, and for determination of a characteristic of the materialwith reference to library data 174. In the present embodiment, cost,size, and energy use of the material monitoring device 200 is reduced bykeeping storage and computation away from the material monitoring device200, and having only measurement and data transmission take place on thematerial monitoring device 200, with a wireless device 130 acting as anintermediary data transport device.

In other embodiments, these responsibilities can be distributedarbitrarily across the material monitoring device 200, wireless device130, and computing device 160, or a cloud computing environment. Forexample, the database 170 comprising library data 174 may be stored on asingle wireless device 130, or may be distributed across severalwireless devices 130, eliminating the need for a computing device 160.Alternatively, a material monitoring device 200 or a plurality ofmaterial monitoring devices 200 may be in direct communication with acomputing device 160 or a plurality of computing devices 160,eliminating the need for a wireless device 130. Furthermore, the personskilled in the art upon reading this specification will appreciate thatstorage, computation, correlation, and machine learning techniques cantake place directly on a single or a plurality of material monitoringdevices 200, on a single or plurality of wireless devices 130, or on asingle or plurality of computing devices 160. In further embodiments, aplurality of material monitoring devices 200 include sufficient storageand communication capability to host a distributed database comprisinglibrary data, and sufficient processing capability to determinecharacteristics of materials and communicate alerts of suchcharacteristics.

It is contemplated that, in some embodiments, the system 100 includes aplurality of material monitoring devices 200 monitoring a plurality ofmaterials 105 at a plurality of material conduits 110, a plurality ofmaterial monitoring devices 200 contributing measurement data 172 tolibrary data 174 for contribution to a machine learning model.

In other applications, materials other than water are monitored. Forexample, it is understood that the materials 105 being monitored cancomprise other fluids, liquids, gases, solids, plasmas, beverages, otheralcohols, foodstuffs, chemicals, chemicals undergoing chemicalreactions, or any other suitable material of interest for whichelectronic or magnetic monitoring would be feasible. The material 105may include beer, liquor, another beverage, a chemical, or any otherfluid. In such embodiments, the conduit 110 comprises piping, tubing,hose, spout, or any other conduit suitable to transport the fluid.

In still other applications, the material 105 includes a solid foodstuffthat is capable of flow through a conduit and is susceptible toelectrical measurements from an electrode and magnetic measurementsthrough a magnetic coil. An example of such a solid foodstuff includesgranulated sugar. In such embodiments, the conduit 110 includes aconveyer, trough, or any other mechanism suitable to transport thesolid. A example of a solid or semi-solid foodstuff is tomato paste.Such a foodstuff may flow through a conduit and may be forced orextruded through a pair of electrodes that perform one or more of theelectrical measurements described herein. Further applications includemeasurement of gas/vapor. Other examples include medical vaccinemonitoring, medication monitoring, or medication authentication.

FIG. 2 depicts a perspective view of a material monitoring device 200,according to a non-limiting embodiment. The material monitoring device200 comprises a body 206 having an interior end 202 and an exterior end204, a sensor device 210 at the interior end 202, and an exteriorindicator 216 at the exterior end 204. With reference to the embodimentin FIG. 1, the material monitoring device 200 can be incorporated intoan attachment to an opening of a water faucet, with sensor device 210oriented toward the material 105 in a manner permitting interaction ofthe sensor device 210 with the material 105, and the exterior indicator216 oriented to be visible to a user of the water faucet.

The sensor device 210 comprises an output electrode 212, an inputelectrode 214, and a magnetic coil 215. The output electrode 212 andinput electrode 214 extend into the material 105. The output electrode212 is used to apply an electrical stimulus to the material 105. Inturn, the input electrode 214 is used to measure an electrical responsesignal of the material 105. The input electrode 214 thus includes areturn-path electrode for completing the electrical connection allowingan electrical response signal to return from the material 105.

The magnetic coil 215 is used to apply a stimulating magnetic field tothe material 105, and is also used to measure a magnetic response signalfrom the material 105.

The output electrode 212 and input electrode 214 may include anysuitable material for electrical conductivity, including gold, agold-plated metal, platinum, a platinum-plated metal, carbon, graphite,graphene, silver, silver chloride, silicon, germanium, tin, iron,copper, or brass, or other suitable materials. Similarly, the magneticcoil may include an electromagnet of any suitable material forgenerating a magnetic field.

The exterior indicator 216 includes at least one of: a simple singlecolor light-emitting diode (LED), a multi-color LED, a moving coilgalvanometer, voltmeter or current meter, a piezoelectric transducer, aspeaker, a buzzer, a siren, a relay switch, an optical bar graph, acounter such as a numerical counter or any suitable counter, liquidcrystal display (LCD), or any other suitable indicator device thatinterfaces with the circuitry of the material monitoring device 200, asdescribed in greater detail below.

In the present embodiment of a system for monitoring characteristics ofwater passing through a water faucet, the exterior indicator 216comprises a two color LED, where the color red indicates the watercontains a contaminant, and the green colour indicates that nocontaminants are detected.

Although in the embodiment of FIG. 1, the material monitoring device 200is attached to opening 115 of conduit 110, it is contemplated that thematerial monitoring device 200 may be located elsewhere along conduit110, for example, along the piping leading to the water faucet.

In some applications for monitoring liquids, the output electrode 212and input electrode 214 need not extend into the liquid, but ratherconducts measurements on the gas/vapor in the headspace above the liquidto infer properties of the liquid.

Although in the present embodiment shown in FIG. 2, the sensor device210 is shown having an input electrode and an output electrode, it iscontemplated that a single electrode may serve as both input and outputelectrode. Furthermore, it is contemplated that the sensor device 210may include an input magnetic coil and an output magnetic coil. Apurpose of sensor magnetic coil 43 is to magnetically couple with theproduct being monitored and optionally allow it to magneticallystimulate the product being monitored and/or optionally measure amagnetic field result from the product being monitored. Moreover, it iscontemplated that the sensor device 210 may include a plurality ofelectrodes, some of the electrodes operating as input electrodes andsome as output electrodes, and that the sensor device 210 may include aplurality of magnetic coils, some of the magnetic coils operating asinput magnetic coils and some as output magnetic coils.

Various further embodiments of the material monitoring device 200 arecontemplated. In one embodiment, the sensor device 210 includes a thirdelectrode. In such an embodiment, the three electrodes are a workingelectrode, a reference electrode, and a counter electrode, thus enablingadditional electro-analytical techniques. For example, the sensor device210 includes a three-electrode potentiostat system for measuring redoxreactions or other types of reactions.

In a further embodiment, the sensor device 210 includes only a singleelectrode for taking measurements without applying any electricalstimulus to the material 105. In such an embodiment, the sensor device210 comprises no output electrode, but only a single input electrode fortaking input measurements.

Similarly, in a further embodiment, the magnetic coil 215 may beconfigured for taking magnetic measurements without applying astimulating magnetic field to the material 105.

In further variations of the material monitoring device 200, theexterior indicator 216 may be omitted. In this variation, the status orcharacteristics of the material 105 may be communicated to and presentedat wireless device 130 or computing device 160.

FIG. 3 depicts functional blocks of the material monitoring device 200,according to a non-limiting embodiment. The material monitoring device200 comprises a sensor device 210 comprising an output electrode 212 aninput electrode 214, and a magnetic coil 215. The material monitoringdevice 200 further comprises an exterior indicator 216, a communicationdevice 230, power supply 222, and circuit 220.

The communication device 230 is configured to transmit datacorresponding to measured electrical and magnetic properties of thematerial 105 to the wireless device 130 and/or computing device 160, asthe case may be. The communication device 230 comprises a communicationsantenna, or any other suitable communication device configurable tocommunicate directly with a wireless device 130 or computing device 160.

The power supply 222 supplies power to the components of the materialmonitoring device 200. In the present embodiment, the power supply 222comprises a power harvesting circuit. The power harvesting circuitharvests electrical power from a communications field or by, in the caseof a material travelling through a conduit, by kinetic power harvestingfrom the motion of the material 105. In other embodiments, the powersupply 222 comprises a battery, a solar cell, or external power supplyconnection, such as an AC or DC connection. Although in the presentembodiment the power supply 222 is illustrated as being housed withinthe body 206 of the material monitoring device 200, in other embodimentsit is contemplated that the power supply could be exterior to the body206.

The circuit 220 comprises circuitry for providing electrical connectionsbetween the sensor device 210, communication device 230, power supply222, and exterior indicator 216. In various embodiments, a portion ofthe circuit 220 forms part of the sensor device 210. Furthermore, insome embodiments, the circuit 220 includes one or more of the following:integrated circuit device power harvesting circuit 52, integratedcircuit device communications radio circuit 54, integrated circuitdevice control state-machine circuit 56, integrated circuit devicesensor output stimulator circuit 58, integrated circuit device sensorinput measurement circuit 60, and integrated circuit device sensormagnetic stimulation and measurement circuit 61, a processor, amicrocontroller, a state machine, a logic gate array, anapplication-specific integrated circuit (ASIC), a system-on-a-chip(SOC), a field-programmable gate array (FPGA), or similar, capable ofexecuting, whether by software, hardware, firmware, or a combination ofsuch, a method for monitoring characteristics of a material as discussedin greater detail below. In the present embodiment, the circuit 220implements a system-on-a-chip (SOC). In some embodiments, the circuit220 includes memory, where measurement data 172 is to be stored on thematerial monitoring device 200, before, or in addition to, beingtransmitted to the wireless device 130 or computing device 160.

In various embodiments, the circuit 220 is a discrete electrical circuitmade up of separate discrete electrical components. In otherembodiments, the circuit 220 includes an ASIC, an FPGA, an SOC, orcombinations thereof. Embodiments of the circuit 220 that include acombination of separate discrete electrical components and an ASIC,FPGA, and/or SOC are also contemplated. In various embodiments, portionsof the circuit 220 that describe a logical state-machine are implementedas software and/or firmware that operate on a processor ormicrocontroller. In various embodiments, the circuit 220 furtherincludes an electrode interface portion that includes circuit elementsspecific to the electrodes for performing electrical stimulation andelectrical measurements, and such circuit elements can be considered tobe part of the sensor device 210.

In some embodiments, the material monitoring device 200 is configured toconduct electrical measurements of the material 105. In suchembodiments, the material monitoring device 200 may conduct impedancespectroscopy, also known as dielectric spectroscopy, for electricallystimulating the material 105 and performing a measurement on thematerial 105. It is to be understood, however, that in otherembodiments, other electro-analytical methodologies can be performed,such as potentiometry, coulometry, voltammetry, square wave voltammetry,stair-case voltammetry, cyclic voltammetry, alternating currentvoltammetry, amperometry, pulsed amperometry, galvanometry, andpolarography, and other suitable electro-analytical methodologies. Invarious embodiments, several of the aforementioned methodologies areused in combination.

In some embodiments, the material monitoring device 200 furthercomprises a sensor capable of taking additional measurements, such asacceleration, position, temperature, pressure, color, light intensity,light phase, density, surface tension, viscosity, resistance, impedance,voltage, current, charge, quantity of mass, quantity and direction offorce, quantum mechanical properties, or any other suitable propertythat can be measured by a sensor. In yet other embodiments, the sensorincludes a gyroscope or magnetometer.

In some embodiments, the material monitoring device 200 comprises asensor with a digital interface designed to perform similarmeasurements, with the sensor interfacing with the circuit 220 throughmethods such as Two Wire Interface (TWI or I2C compatible), SPIinterface, Microwire, 1-Wire, Single Wire Protocol (SWP), or any othersuitable digital or analog communications methodologies.

The circuit 220 may control operations of the material monitoring device200, including initializing the circuit 220 with required startupparameters, initiating and recording measurements of the sensor device210, packetizing the measurement data 172 into data packets, controllingthe communication device 230 for the reception and transmission of data,commands, and ancillary information, any firmware or software updates,and any other suitable information being transmitted or received.

FIG. 4 depicts a flowchart of a method 400 for determining acharacteristic of a material, according to a non-limiting embodiment.The method 400 is one way in which the characteristics of a material canbe monitored. It is to be emphasized, however, that the blocks of method400 need not be performed in the exact sequence as shown. The method 400is described as performed by a system and device discussed herein, butthis is not limiting and the method can alternatively be performed byother systems and/or devices.

With reference to FIG. 5, and with continued reference to FIG. 4, thegeneration and measurement of electrical and magnetic signals, asdescribed in method 400, are diagrammed schematically.

At block 402, an initiating electrical signal 502 is generated andtransmitted. In the present embodiment, the initiating electrical signal502 is generated on the material monitoring device 200, and istransmitted to the output electrode 212 and magnetic coil 215 on thematerial monitoring device 200. Transmission of the initiatingelectrical signal 502 to the output electrode 212 generates anelectrical stimulus 504. Transmission of the initiating electricalsignal 502 to the magnetic coil 215 generates a stimulating magneticfield 506.

It is to be understood that in other embodiments, the two or moreinitiating electrical signals 502 may be generated, one for transmissionto output electrode 212, another for transmission to the magnetic coil215. Furthermore, it is to be understood that the initiating electricalsignal 502 may be generated elsewhere in system 100, such as from acomputing device 160, and transmitted to material monitoring device 200.

The electrical stimulus 504 may be referred to as an electricalelectrode stimulation signal profile (EESSP). In some embodiments, theEESSP may comprise a varying signal profile developed to excite thematerial 105. Such varying signals may include a continuous, discrete,periodic, or an aperiodic signal, or combinations thereof.

In some embodiments, the EESSP may comprise a dynamic AC signal or astatic DC signal. In embodiments in which the EESSP comprises a dynamicAC signal, the EESSP may include a sinusoidal oscillating signal. Thesinusoidal oscillating signal may be continuous and periodic for aduration sufficient to stimulate the material 105 such that anelectrical response signal 508 may be measured. The EESSP may be variedin amplitude, frequency, or other properties. In some embodiments, theEESSP may be generated from a voltage source. In other embodiments, theEESSP may be generated from a current source.

The stimulating magnetic field 506 may be referred to as a magnetic coilstimulation signal profile (MCSSP). In some embodiments, the MCSSP maycomprise a varying signal developed to excite the material 105. Suchvarying signals may include a continuous, discrete, periodic, oraperiodic signal, or combinations thereof.

In some embodiments, the MCSSP may comprise a dynamic AC signal or astatic DC signal. In embodiments in which the MCSSP comprises a dynamicAC signal, the MCSSP may include a sinusoidal oscillating signal. Thesinusoidal oscillating signal may be continuous and periodic for aduration sufficient to stimulate the material 105 such that a magneticresponse signal 510 may be measured. The MCSSP may be varied inamplitude, frequency, or other properties. In some embodiments, theMCSSP may be generated from a voltage source. In other embodiments, theMCSSP may be generated from a current source.

In some embodiments, the MCSSP may comprise a uniform magnetic field.

At block 404, the electrical stimulus 504 is applied to material 105 byoutput electrode 212.

At block 406, the stimulating magnetic field 506 is applied to material105 by magnetic coil 215.

At block 408, an electrical response signal 508, detected from material105, is measured by input electrode 214 as electrical response signalmeasurement 512. The electrical response signal 508 and thus theelectrical response signal measurement 512 is influenced by theelectrical stimulus 504 being altered by the material 105. Theelectrical response signal 508 or electrical response signal measurement512 may be referred to as an electrical electrode receiving signalprofile (EERSP). In some embodiments, the EERSP may be analyzed furtherin its raw form. In some embodiments, the EERSP may be processed with amathematical transform for further use in further analysis. Themathematical transforms that may be applied to the EERSP include Fouriertransform, Fast Fourier Transform (FFT), Discrete Fourier Transform(DFT), Laplace transform, Z transform, Hilbert transform, DiscreteCosine transform, wavelet transform, discrete wavelet transform,Infinite Impulse Response (IIR), Finite Impulse Response (FIR), or theirdiscrete or accelerated variants, or other mathematical transforms. Themathematical transform can be made in any possible domain such, as butnot limited to, time and space domain, frequency domain, Z-planeanalysis (Z-domain), and Wavelet analysis, and any such relevant domainor analysis methodology.

At block 410, a magnetic response signal 510, detected from material105, is measured by magnetic coil 215 as magnetic response signalmeasurement 514. The magnetic response signal 510 and thus the magneticresponse signal measurement 514 is influenced by the stimulatingmagnetic field 506 being altered by the material 105. The magneticresponse signal 510 or magnetic response signal measurement 514 may bereferred to as a magnetic coil receiving signal profile (MCRSP). In someembodiments, the MCRSP may be analyzed further in its raw form. In someembodiments, the MCRSP may be processed with a mathematical transformfor further use in further analysis. The mathematical transforms thatmay be applied to the MCRSP include Fourier transform, Fast FourierTransform (FFT), Discrete Fourier Transform (DFT), Laplace transform, Ztransform, Hilbert transform, Discrete Cosine transform, wavelettransform, discrete wavelet transform, Infinite Impulse Response (IIR),Finite Impulse Response (FIR), or their discrete or acceleratedvariants, or other mathematical transforms. The mathematical transformcan be made in any possible domain such, as but not limited to, time andspace domain, frequency domain, Z-plane analysis (Z-domain), and Waveletanalysis, and any such relevant domain or analysis methodology.

In some embodiments, the material monitoring device 200 conductsmeasurements at regular intervals, as some applications require a delaytime in order to perform a suitable measurement. In one such embodiment,the wireless device 130 sends instructions to material monitoring device200 to conduct a measurement at an interval. In another such embodiment,the computing device 160 sends instructions to material monitoringdevice 200 to conduct a measurement at an interval.

In some embodiments, the electrical response signal 508 and the magneticresponse signal 510 are included in measurement data 172. In someembodiments, initiating electrical signal 502 is included in measurementdata 172.

At block 412, the measurement data 172 is packetized for transmission toan external computing device. In embodiments in which the circuit 220comprises memory, the measurement data 172 is recorded on memory beforetransmission.

At block 414, measurement data 172 is transmitted by the communicationdevice 230 to the wireless device 130, which in turn transmits themeasurement data 172 to the computing device 160, which stores themeasurement data 172 on database 170.

At block 416, the measurement data 172 transmitted at block 340 iscontributed to the library data 174 in database 170. In otherembodiments in which the measurement data 172 is not contributed to thelibrary data 174, this block is omitted.

At block 418, measurement data 172 is analyzed at the computing device160. In the present embodiment, measurement data 172 is analyzed bymachine learning model 550.

Although in the present embodiment, the machine learning model 550 islocated at the computing device 160, it is emphasized that machinelearning, and any analysis at block 418, can take place at a wirelessdevice 130, the material monitoring device 200, or a computing device160, or can be arbitrarily distributed across monitoring devices 200,wireless devices 130, and computing devices 160, or a cloud computingenvironment.

At block 420, a characteristic of the material 105 is determined basedon the analysis at block 418.

Where a machine learning model 550 is applied in the analysis ofmeasurement data 172 at block 418, several machine learning techniquesmay be applied. In one such embodiment, a neural network algorithm thatemploys a Bayesian algorithm and a decision tree analysis to classifythe measurement data 172 and report the classified result in order toclassify the characteristics of the material 105.

In another embodiment, principal component analysis (PCA) is used on themeasurement data 172 to report on the status of the material 105 andalso classify its characteristics.

In another embodiment, principal component regression (PCR) is used onthe measurement data 172 to report on the status of the material 105 andalso classify its characteristics.

In other embodiments, other suitable data analysis techniques may beused, such as clustering analysis, correlation, neural network machinelearning algorithms, support vector machine algorithms, random forestalgorithms, convolution neural network algorithms, deep belief networks,deep QA networks, or other appropriate algorithms. Machine learningalgorithms may include supervised machine learning algorithms orunsupervised machine learning algorithms.

It is to be emphasized that the material monitoring device includes atleast one electrode and at least one magnetic coil for measuringelectrical and magnetic signals from the material. In some embodiments,an electrical stimulus 504 is applied without a stimulating magneticfield 506, where an electrical response signal 508 may be measuredalone, a magnetic response signal 510 may be measured alone, or both anelectrical response signal 508 and magnetic response signal 510 may bemeasured. In some embodiments, a stimulating magnetic field 506 isapplied without an electrical stimulus 504, where an electrical responsesignal 508 may be measured alone, a magnetic response signal 510 may bemeasured alone, or both an electrical response signal 508 and magneticresponse signal 510 may be measured. In some embodiments, both anelectrical stimulus 504 and a stimulating magnetic field 506 areapplied, simultaneously or sequentially in any scheme, where anelectrical response signal 508 may be measured alone, a magneticresponse signal 510 may be measured alone, or both an electricalresponse signal 508 and magnetic response signal 510 may be measured. Insome embodiments, a plurality of electrodes, or a plurality of magneticcoils, may be used, with some electrodes or magnetic coils beingdedicated to providing a stimulus, with others being dedicated tomeasurement. In still other embodiments, no electrical stimulus 504 isapplied, and no stimulating magnetic field 506 is applied, where anelectrical signal alone, a magnetic signal alone, or both, are measured.

Furthermore, it is emphasized some of the blocks of method 400 need notbe performed in the exact sequence as shown. For example, the stimulusapplication in blocks 404 and 406 may be executed simultaneously and themeasurement in blocks 408 and 410 may be executed simultaneously.

Furthermore, blocks of the method 400 may thus be omitted or repeated.For example, where the material monitoring device 200 comprises a singleelectrode, blocks 404 and 408 are replaced with a block at which ameasurement is taken.

Although in the present embodiment, machine learning techniques areapplied at block 418, other forms of analysis may be used. For example,a polynomial regression may be used on the measurement data 172 toreport on the status of the material 105 and also classify itscharacteristics. Linear regression and non-linear regression may also beused.

In some embodiments, the material monitoring device 200 may vary theelectrical stimulus 504 (EESSP) or the stimulating magnetic field 506(MCSSP) over time. In some embodiments, the EESSP and MCSSP may bevaried simultaneously. In some embodiments, the EESSP or MCSSP may bevaried independently. The EESSP or MCSSP may be varied through aspectrum of any property of interest. For example, the EESSP may bevaried through a band of amplitude, while the MCSSP is varied through aband of amplitude. Any combination of variation of EESSP or MCSSP in anydimension, together or independently, are contemplated. A robust datasetof electrical response signals 508 (EERSP) and magnetic response signals510 (MCRSP) can thus be gathered for inclusion into and analysis by themachine learning model 550 for determination of a particular family ofmaterials having particular characteristics.

Thus, by application of method 400, a characteristic of a material 105being monitored is determined with reference to the electricalproperties or the magnetic properties of the material 105. Thesecharacteristics, although not measurable directly, are recognized by amachine learning algorithm incorporating measurement data 172 andlibrary data 174, which relates electrical properties and magneticproperties of a material to known characteristics of the material. Byapplication of method 400, the library data 174 is expanded withadditional data relating electrical properties and magnetic propertiesof materials to characteristics of materials.

FIG. 6 depicts a flowchart of a method 600 for initializing a materialmonitoring device 200, according to a non-limiting embodiment. Themethod 600 is one way in which a material monitoring device can beinitialized. It is to be emphasized, however, that the blocks of method600 need not be performed in the exact sequence as shown. The method 600is described as performed by a system and device discussed herein, butthis is not limiting and the method can alternatively be performed byother systems and/or devices.

In the present embodiment, the material monitoring device 200 remains inan idle state with low energy consumption between conductingmeasurements. When instructed to conduct a measurement, the materialmonitoring device 200 undergoes a process of initialization to prepareto conduct a measurement. Upon concluding conducting a measurement, thematerial monitoring device 200 returns to an idle state.

At block 602, an instruction to conduct a measurement is received by thecommunication device 230 from an external computing device such as thewireless device 130 or computing device 160.

At block 604, it is determined whether the material monitoring device200 has sufficient electrical power to conduct a measurement. Ifsufficient power is present, block 606 is executed. If sufficient poweris not present, block 614 is executed. Whether sufficient electricalpower is present may be determined by whether a suitable electricalconnection is established with an outside power source, whethersufficient battery power is remaining, or whether the energy harvestingcircuit has harvested sufficient power for operation.

At block 606, circuit parameters are initialized. For example,initialization includes initializing one or more parameters such as:processor or system clock frequency, analog circuit gain, analog circuitdrive strength, analog circuit termination impedance, stimulationvalues, delay values, filter settings, and any other suitableprogrammable setting in the device. The aforementioned list ofparameters is non-limiting and other parameters are contemplated.

At block 608, a characteristic of material 105 is determined asdescribed with respect to method 400 in FIG. 4 above.

At block 610, it is determined whether sensor regeneration is required.If sensor regeneration is required, block 612 is executed. If sensorregeneration is not required, block 614 is executed. Some sensorsrequire a special regeneration cycle, and others do not, as will beapparent to the person skilled in the art upon reading thisspecification. For example, a three-electrode potentiostat measurementsystem that uses very sensitive electrodes may require a regenerationcycle to free ions from the electrode that may collect on the electrodeduring the measurement cycle.

At block 614, the material monitoring device 200 is in an idle statewith low energy consumption. In the present embodiment where the powersupply 222 is a power harvesting circuit, the material monitoring device200 waits until sufficient power is harvested for a measurement to beconducted.

It will be understood by the person skilled in the art upon reading thisspecification that it is possible to add or omit blocks as necessary toexecute any given measurement algorithm.

FIG. 7 depicts functional blocks of a material monitoring device 700,according to a non-limiting embodiment. The material monitoring device700 includes a sensor device 710 having an output magnetic coil 712, aninput magnetic coil 714, and an electrode 715. The output magnetic coil712 is generates and applies a stimulating magnetic field to a material105, and the input magnetic coil 714 is dedicating to measuring amagnetic response signal. The electrode 715 operates to both apply anelectrical stimulus to the material 105 and measure an electricalresponse signal.

With regard to the body 706, communication device 730, circuit 720,power supply 722, and exterior indicator 716, reference may be had tothe description of analogous components in FIG. 3.

FIG. 8 shows a system 800 for monitoring a material 805, according to anon-limiting embodiment. System 800 includes one or more materialvessels 810 having vessel openings 815 and containing a material 805. Inthe present embodiment, the material 805 comprises wine, and thematerial vessel 810 comprises a wine bottle. System 800 includes awireless device 830, network 850, computing devices 860, database 870,measurement data 872, and library data 874, for which reference may behad to the description of analogous components in FIG. 1 and thedisclosure above.

FIG. 9 depicts a perspective view of the material monitoring device 900,according to a non-limiting embodiment. Material monitoring device 900includes a body 906, an interior end 902, an exterior end 904, a sensordevice 910 having an output electrode 912, an input electrode 914, amagnetic coil 915, and an external indicator, for which reference may behad to the description of analogous components in FIG. 2 and thedisclosure above.

The material monitoring device 900 can be incorporated into a wine corkplugging the vessel opening 815 of material vessel 810. The body 906 canbe sized to plug the opening 815 of the material vessel 810. In thepresent embodiment for monitoring wine in a wine bottle, the body 906comprises a wine bottle cork sized to plug the opening 815 of the winebottle. However, in other embodiments, the body 906 comprises a barrelbung, a cap, a lid, or an attachment embedded into the side of a vessel,or any other stopper, or means for housing a material monitoring device900 with a sensor device 910 for measurement of the material 805 beingmonitored. The material of the body 906 comprises any material suitablefor the particular application, such as plastic, natural cork, syntheticcork, agglomerated cork, or wax for the wine bottle application.

In the present embodiment of a system for monitoring characteristics ofwine in a wine bottle, when disposed within the opening of a winebottle, the interior end 902 of the material monitoring device 900 isoriented toward the wine, with the sensor device 910 protruding from theinterior end 902, and with output electrode 912 and input electrode 914extending into the wine contained within the wine bottle.

A sensor device of material monitoring device 900 may thereby measureelectrical or magnetic properties of the material 805, and may haveelectrodes in direct contact with the material 805, or in contact withthe gas/vapor in the headspace above the liquid to infer properties ofthe material 805, as discussed above throughout this disclosure.

An advantage of housing the material monitoring device 900 within a winebottle cork is that the wine bottle need not be opened, and thusdisturbed, in order to inspect the wine for a characteristic. Further,in the present embodiment of monitoring the characteristics of wine, thesystem 800 could be used to monitor whether the wine is within theoptimal taste window or outside of the optimal taste window.

In the present embodiment of a system for monitoring characteristics ofwine in a wine bottle, the external indicator 916 comprises a threecolor LED, where the color red indicates the wine has passed its optimalpoint of consumption, the color yellow indicates the wine approachingthe end of its optimal point of consumption, and the green colourindicates that the wine is within its optimal point of consumption.

In some embodiments, canonical correlation is used on the measurementdata 872 to report on the status of the material 805, including, in thecase of monitoring the characteristics of wine, whether the wine iswithin the wine's optimal taste window or approaching its expiry point,and an estimate of how much time may be left before the wine is expectedto reach its expiry point.

Although the present example discusses an application to monitoring winein a wine bottle, wine in a wine bottle is merely one example.Implementations are not limited to monitoring a particular class ofmaterials, whether the material is a fluid, liquid, gas, solid,beverage, foodstuff, chemical, and the vessel is not limited to aparticular class of vessel. In addition, other types of containers anddelivery conduits instead of vessels are contemplated, such as cartons,packages, kegs, water pipes, water bottles, water containers (e.g.,office-style water coolers), to name a few.

In other embodiments, materials other than wine are monitored. Forexample, it is understood that the materials 805 being monitored cancomprise fluids, liquids, gases, solids, plasmas, beverages, otheralcohols, foodstuffs, chemicals, chemicals undergoing chemicalreactions, or any other suitable material of interest for whichelectronic monitoring would be feasible. Other examples include medicalvaccine monitoring, medication monitoring, or medication authentication.Furthermore, the material vessels 810 includes wine bottles, winebarrels, bottles or barrels of other alcohols, casks, or beveragecontainers of any kind which can fit a material monitoring device 900.

In other embodiments, wine undergoing a fermentation process in a barrelis monitored via a material monitoring device 900 embedded within thebung of the barrel, or in another suitable location, for indicating thelevel of completion of the fermentation cycle. Additionally, the agingprocess of wine can be monitored, with an alert being sent to thewireless device 830 to indicate that the wine has completed its agingprocess and it is ready to ship to market. Additional characteristics ofwine that could be monitored, whether in a bottle or aging in a barrel,include sweetness of flavor, acidity, tannin, fruitiness of flavor,body, aroma, or any other suitable characteristic of wine that isusually measured. These characteristics, although not measurabledirectly, can be inferred from comparing measurement data 872 to librarydata 874, which relates electrical properties of wines to knowncharacteristics of wines.

It should be apparent from the above that characteristics of a materialcan be monitored via the electrical and magnetic properties of thematerial by a low-power, compact, material monitoring device capable ofdirect yet non-invasive contact with a material, locatable at a conduitor a vessel, in cooperation with a machine learning model fordetermining a characteristic of a material using an evolving model basedon machine learning techniques.

Characteristics of a material may also be monitored by periodicallytaking measurements of the material using dedicated sensor devices, suchas a pH sensor, temperature sensor, humidity sensor, and the like, andcorrelating such measurements to a related characteristic of thematerial in known ways. For example, the it may be known that the pH oftap water may be related to its mineral content, and thus adetermination of the mineral content of a sample of water may be madewith reference to its pH. However, such monitoring techniques arelimited in that they rely on known relationships between a measurementand a characteristic. In contrast, by taking measurements of a materialthat is not known to relate to a particular characteristic, e.g. bytaking measurements related to electrical or magnetic properties of amaterial, which provides a broader dataset for analysis than a dedicatedsensor device, it may be determined that a particular feature of anelectrical signal profile, or a particular feature of a magnetic signalprofile, relates to a characteristic of the material that is notdirectly measurable, and relates in a manner which may not have beenpreviously known, or which may not be expressible in the form of a knownrelationship, such as how the pH level of water is known to be impactedby its mineral content. Further, by considering the connections betweenelectrical properties of a material and magnetic properties of thematerial, a richer dataset for analysis is provided. For example,electrical stimulation of the material may have a measurable effect onthe magnetic properties of the material, which can be recognized by amachine learning model to indicate a particular characteristic thatwould not otherwise be directly measurable. Thus, a more expansivesystem for monitoring the characteristics of a material is provided.

The scope of the claims should not be limited by the embodiments setforth in the above examples, but should be given the broadestinterpretation consistent with the description as a whole.

1.-22. (canceled)
 23. A communications device for monitoring acharacteristic of a material, the communications device comprising: asensor device, the sensor device comprising at least one electrodeconfigured to provide an electrical stimulus to the material and atleast one magnetic coil configured to provide a magnetic stimulus to thematerial, the sensor device configured to measure at least one or moresignals responsive to at least one or more of: the electrical stimulus,and the magnetic stimulus, the signal relating to an electrical propertyof the material; an integrated circuit electrically connected to thesensor device, the integrated circuit to communicate measurement datarelated to the at least one signal to a processor via a network, whereinthe processor is configured to apply machine learning for determining anot directly measurable characteristic of the material based on themeasurement data received from the integrated circuit, the machinelearning applied via a machine learning model trained with library datato recognize the not directly measurable characteristic of the material,the library data relating previously measured signals relating theelectrical property of the material to known not directly measurablecharacteristics of the material, and a power source to power the sensordevice and the integrated circuit; and a body containing the sensordevice and the integrated circuit, the body positionable with respect tothe material to position one of the at least one electrode and the atleast one magnetic coil to interact with the material.
 24. Thecommunications device of claim 23, wherein the signal is further relatedto a magnetic property of the material.
 25. The communications device ofclaim 23, wherein the body is attachable to a material conduit fortransporting the material, the body comprising: the at least oneelectrode of the sensor device extending into an interior of thematerial conduit; and the at least one magnetic coil positioned tomagnetically couple with the material.
 26. The communications device ofclaim 23, wherein the body comprises a stopper configured to plug anopening of a vessel, the vessel defining an interior for containing thematerial, wherein the stopper is configured to position one of the atleast one electrode and the at least one magnetic coil, wherein when thestopper is configured to position the electrode, the electrode extendsinto an interior of the vessel, and wherein when the stopper isconfigured to position the at least one magnetic coil, the magnetic coilmagnetically couples with the material.
 27. The communications device ofclaim 26, wherein the vessel comprises a wine bottle, the materialcomprises wine, and the stopper comprises a wine bottle cork.
 28. Thecommunications device of claim 23, wherein the power source is selectedfrom a group consisting of: a power harvesting circuit, a battery, asolar cell, and an alternating current electrical power adapter.
 29. Thecommunications device of claim 23, wherein one or more of the sensordevice, the integrated circuit, and the processor are configured toperform an analytical methodology selected from a group consisting of:potentiometry, coulometry, voltammetry, impedance spectroscopy, squarewave voltammetry, stair-case voltammetry, cyclic voltammetry,alternating current voltammetry, amperometry, pulsed amperometry,galvanometry, and polarography.
 30. The communications device of claim23, wherein the material is selected from a group consisting of: afluid, a liquid, a gas, a vapor, a plasma, and a solid.
 31. The use ofthe communications device of claim 23 for chemical monitoring, vaccinemonitoring, medication monitoring, medication authentication, winemonitoring, foodstuffs monitoring, water monitoring, and the monitoringof chemicals undergoing a chemical reaction.
 32. The communicationsdevice of claim 23 uses a signal to drive the magnetic coil to produce astimulus, wherein the signal is one of a static signal and a dynamicsignal.
 33. The communications device of claim 32 wherein the signal isa dynamic signal selected from a list consisting of: a sine wave, asquare wave, a series of pulses, a complex signal that repeats apattern, and a complex signal that does not repeat a pattern.
 34. Thecommunications device of claim 23 further comprising one of a displaydevice and an audio device.
 35. The use of the communications device ofclaim 23 for communicating over a network, wherein the network is incommunication with one or more of a wireless network and a cloudcomputing environment.
 36. The use of the communications device of claim23 for communicating with a mobile device.
 37. The communications deviceof claim 23, wherein the sensor device further comprises a sensor formeasuring a quantum mechanical property.
 38. A non-transitorymachine-readable medium comprising instructions that when executed causea processor of a computing device to: apply machine learning todetermine a not directly measurable characteristic of a material basedon at least one signal associated with an electrical property of thematerial; wherein the signal associated with the electrical property ofthe material is measured by an electrode in response to a stimulusprovided by the electrode or a magnetic coil, wherein measurement datarelated to the at least one signal is transmitted to the processor via anetwork from an integrated circuit connected to the at least oneelectrode and the at least one magnetic coil and housed within the abody which houses the at least one electrode and the at least onemagnetic coil; and wherein the machine learning is applied via a machinelearning model trained with library data to recognize the not directlymeasurable characteristic of the material, the library data relatingpreviously measured signals associated with the at least one electricalproperty of the material to known not directly measurablecharacteristics of the material.
 39. The non-transitory machine-readablemedium of claim 38 further comprising instructions that when executedcause the processor to apply machine learning to determine the notdirectly measurable characteristic of the material based on a signalassociated with a magnetic property of the material; wherein the signalassociated with the magnetic property of the material is measured by themagnetic coil in response to the stimulus; and wherein the library datafurther relates to previously measured signals associated with themagnetic property of the material to the known not directly measurablecharacteristics of the material.
 40. The use of the non-transitorymachine-readable storage medium of claim 38 for chemical monitoring,vaccine monitoring, medication monitoring, medication authentication,wine monitoring, foodstuffs monitoring, water monitoring, and themonitoring of chemicals undergoing a chemical reaction.
 41. Thenon-transitory machine-readable storage medium of claim 38, wherein theat least one electrode and the at least one magnetic coil is configuredto interact with the material.
 42. The non-transitory machine-readablestorage medium of claim 38 further comprising instructions that whenexecuted cause the processor to control an external indicator to displaydata representing the not directly measurable characteristic of thematerial recognized by the machine learning model, the externalindicator is one of a display device and an audio device.
 43. Thenon-transitory machine-readable storage medium of claim 38 furthercomprising instructions that when executed cause the processor tocommunicate with one or more of a wireless network and a cloud computingenvironment.
 44. The non-transitory machine-readable storage medium ofclaim 38 further comprising instructions that when executed cause theprocessor to control a power supply, the power supply comprising one ofa power harvesting circuit and a battery.
 45. The non-transitorymachine-readable storage medium of claim 38, wherein the processorcomprises a mobile device.
 46. The non-transitory machine-readablestorage medium of claim 38 further comprising instructions that whenexecuted cause the processor to apply machine learning to determine anot directly measurable characteristic of a material based on at leastone signal associated with a quantum mechanical property of thematerial.
 47. A communications system for monitoring a characteristic ofa material, the system comprising: a communications device comprising: asensor device comprising an electrode to provide an electrical stimulusto a material and a magnetic coil to provide a magnetic stimulus to amaterial and to measure a signal responsive to at least one of theelectrical stimulus and the magnetic stimulus, the signal associatedwith an electrical property of the material; an integrated circuitelectrically connected to the sensor device, the integrated circuit totransmit measurement data related to the signal via a network; a powersource to power the sensor device and the integrated circuit; and a bodycontaining the sensor device and the integrated circuit, the bodypositionable with respect to the material to position the electrode andthe magnetic coil to interact with the material; and a processor tocommunicate with the communications device via the network, theprocessor configured to apply machine learning for determining a notdirectly measurable characteristic of the material based on themeasurement data received from the integrated circuit, the machinelearning applied via a machine learning model trained with library datato recognize the not directly measurable characteristic of the material,wherein the library data relates previously measured signals associatedwith the at least one electrical property of the material to known notdirectly measurable characteristics of the material.
 48. Thecommunications system of claim 47 wherein the signal is further relatedto a magnetic property of the material, and wherein the library datafurther relates previously measured signals associated with the magneticproperty of the material to the known not directly measurablecharacteristics of the material.